New Second-Level-Discrete Zeroing Neural Network for Solving Dynamic Linear System  

作　　者：Min Yang 

机构地区：[1]School of Robotics,Hunan University,Changsha 410082,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第6期1521-1523,共3页自动化学报（英文版）

基　　金：supported in part by the National Natural Science Foundation of China(62303174);the Fundamental Research Funds for the Central Universities(531118010815);the Changsha Municipal Natural Science Foundation(kq2208043).

摘　　要：Dear Editor,This letter deals with a new second-level-discretization method with higher precision than the traditional first-level-discretization method.Specifically,the traditional discretization method utilizes the first-order time derivative information,and it is termed first-level-discretization method.By contrast,the new discretization method not only utilizes the first-order time derivative information,but also makes use of the second-order derivative information.By combining the new second-level-discretization method with zeroing neural network(ZNN),the second-level-discrete ZNN(SLDZNN)model is proposed to solve dynamic(i.e.,time-variant or time-dependent)linear system.Numerical experiments and application to angle-of-arrival(AoA)localization show the effectiveness and superiority of the SLDZNN model.

关 键 词：method DERIVATIVE utilize 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] O241.6[自动化与计算机技术—控制科学与工程]